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Empirical analysis of productivity enhancement strategies in the North American automotive industry

机译：北美汽车行业生产率提高策略的实证分析

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摘要

Even though there is a pressing need for continuous productivity improvement, studies that employ robust empirical analysis of strategies and factors to enhance productivity in the North American Automotive Industry are very scarce. In this study, robust and hybrid models of the most popular productivity measurement in the automotive industry, Hours per Vehicle (HPV), is developed. Data examined in this research was compiled for a 9-year period, 1999-2007, for North American automotive manufacturers. Through practical considerations and a comprehensive literature review, 14 important variables that influence HPV were defined and developed. A hybrid method, the combination of multiple M-estimators and a lasso, was developed and was shown to be the best method to determine a robust regression model to estimate HPV. The vehicle variety, number of available working days in a year, car model types, new model launch, and car assembly and capacity utilization penalize HPV. However, annual production volume, flexible and lean manufacturing, platform sharing strategy, and year of production improve HPV. Using lean and flexible manufacturing, platform strategy, and reducing the percentage of hourly employees help improve productivity and reduce HPV while launching a new vehicle. Additionally, Japanese plants were appeared to be the benchmark with respect to HPV, followed by joint ventures and American plants.
机译：尽管迫切需要持续提高生产率，但对于北美汽车行业中采用有效的策略和因素进行实证分析以提高生产率的研究仍然很少。在本研究中，开发了汽车行业中最流行的生产率测量的鲁棒和混合模型，即每车时数（HPV）。本研究调查的数据是针对北美汽车制造商的9年时间段（1999年至2007年）。通过实际考虑和全面的文献综述，定义并开发了14种影响HPV的重要变量。开发了一种混合方法，将多个M估计量和套索结合在一起，被证明是确定估计HPV的可靠回归模型的最佳方法。车辆种类，一年中的可用工作天数，车辆型号，新车型发布以及车辆组装和产能利用率不利于HPV。但是，年产量，灵活而精益的制造，平台共享策略以及生产年份可以提高HPV。使用精益和灵活的制造，平台策略以及减少每小时工时的百分比有助于提高生产率，并在推出新车时降低HPV。此外，日本工厂似乎是HPV的基准，其次是合资企业和美国工厂。

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